Overview

Dataset statistics

Number of variables32
Number of observations1851
Missing cells109
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory462.9 KiB
Average record size in memory256.1 B

Variable types

CAT21
NUM11

Warnings

Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
Fstf has 109 (5.9%) missing values Missing
SpatAvg is uniformly distributed Uniform
TLCar is uniformly distributed Uniform
TLHGV is uniformly distributed Uniform
df_index has unique values Unique
TempDist has 845 (45.7%) zeros Zeros
SpatDist has 1568 (84.7%) zeros Zeros
UArt1 has 63 (3.4%) zeros Zeros
AUrs1 has 1660 (89.7%) zeros Zeros
AUrs2 has 1840 (99.4%) zeros Zeros

Reproduction

Analysis started2020-11-30 18:58:07.012568
Analysis finished2020-11-30 18:58:28.978613
Duration21.97 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1851
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean933.5440303
Minimum0
Maximum1866
Zeros1
Zeros (%)0.1%
Memory size14.5 KiB
2020-11-30T19:58:29.050421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile92.5
Q1462.5
median936
Q31402.5
95-th percentile1773.5
Maximum1866
Range1866
Interquartile range (IQR)940

Descriptive statistics

Standard deviation540.8130821
Coefficient of variation (CV)0.5793118103
Kurtosis-1.208745946
Mean933.5440303
Median Absolute Deviation (MAD)470
Skewness-0.003498815736
Sum1727990
Variance292478.7898
MonotocityStrictly increasing
2020-11-30T19:58:29.210344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
186510.1%
 
122810.1%
 
123210.1%
 
123410.1%
 
123610.1%
 
123810.1%
 
124010.1%
 
124210.1%
 
124410.1%
 
124610.1%
 
Other values (1841)184199.5%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
186610.1%
 
186510.1%
 
186410.1%
 
186310.1%
 
186210.1%
 

TempMax
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
(8.999, 69.0]
473 
(69.0, 117.0]
470 
(211.5, 1341.0]
463 
(117.0, 211.5]
445 
ValueCountFrequency (%) 
(8.999, 69.0]47325.6%
 
(69.0, 117.0]47025.4%
 
(211.5, 1341.0]46325.0%
 
(117.0, 211.5]44524.0%
 
2020-11-30T19:58:29.384417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:29.470180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:29.594994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length13
Mean length13.74068071
Min length13

TempAvg
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
(2.999, 32.0]
474 
(32.0, 55.0]
460 
(90.0, 1326.0]
460 
(55.0, 90.0]
457 
ValueCountFrequency (%) 
(2.999, 32.0]47425.6%
 
(32.0, 55.0]46024.9%
 
(90.0, 1326.0]46024.9%
 
(55.0, 90.0]45724.7%
 
2020-11-30T19:58:29.773156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:29.887897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:30.003869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length13
Mean length12.75310643
Min length12

SpatMax
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
(831.999, 4506.0]
466 
(8335.0, 14367.0]
463 
(14367.0, 49765.0]
462 
(4506.0, 8335.0]
460 
ValueCountFrequency (%) 
(831.999, 4506.0]46625.2%
 
(8335.0, 14367.0]46325.0%
 
(14367.0, 49765.0]46225.0%
 
(4506.0, 8335.0]46024.9%
 
2020-11-30T19:58:30.124549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:30.211749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:30.324507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length17
Mean length17.0010805
Min length16

SpatAvg
Categorical

UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
(2002.5, 3387.0]
464 
(5264.0, 17805.0]
463 
(134.999, 2002.5]
463 
(3387.0, 5264.0]
461 
ValueCountFrequency (%) 
(2002.5, 3387.0]46425.1%
 
(5264.0, 17805.0]46325.0%
 
(134.999, 2002.5]46325.0%
 
(3387.0, 5264.0]46124.9%
 
2020-11-30T19:58:30.472032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:30.689398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:30.808062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length17
Mean length16.50027012
Min length16

TempDist
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.566180443
Minimum0
Maximum24
Zeros845
Zeros (%)45.7%
Memory size14.5 KiB
2020-11-30T19:58:30.950480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q39
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.88910785
Coefficient of variation (CV)1.23767239
Kurtosis0.1456204672
Mean5.566180443
Median Absolute Deviation (MAD)3
Skewness1.103172777
Sum10303
Variance47.45980697
MonotocityNot monotonic
2020-11-30T19:58:31.157686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
084545.7%
 
6884.8%
 
7764.1%
 
5703.8%
 
8673.6%
 
9663.6%
 
10603.2%
 
3583.1%
 
12512.8%
 
4502.7%
 
Other values (15)42022.7%
 
ValueCountFrequency (%) 
084545.7%
 
1412.2%
 
2321.7%
 
3583.1%
 
4502.7%
 
ValueCountFrequency (%) 
24271.5%
 
23211.1%
 
22271.5%
 
21311.7%
 
20191.0%
 

SpatDist
Real number (ℝ≥0)

ZEROS

Distinct220
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.50567261
Minimum0
Maximum2000
Zeros1568
Zeros (%)84.7%
Memory size14.5 KiB
2020-11-30T19:58:31.327200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile650.5
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation281.6624937
Coefficient of variation (CV)3.294079622
Kurtosis18.39684028
Mean85.50567261
Median Absolute Deviation (MAD)0
Skewness4.148671881
Sum158271
Variance79333.76037
MonotocityNot monotonic
2020-11-30T19:58:31.491942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0156884.7%
 
250150.8%
 
75080.4%
 
125060.3%
 
5030.2%
 
15130.2%
 
29030.2%
 
17030.2%
 
46820.1%
 
34120.1%
 
Other values (210)23812.9%
 
ValueCountFrequency (%) 
0156884.7%
 
210.1%
 
320.1%
 
710.1%
 
1310.1%
 
ValueCountFrequency (%) 
200020.1%
 
197510.1%
 
196010.1%
 
194910.1%
 
190610.1%
 

Coverage
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
(4.999, 28.0]
492 
(41.0, 59.0]
484 
(28.0, 41.0]
440 
(59.0, 100.0]
435 
ValueCountFrequency (%) 
(4.999, 28.0]49226.6%
 
(41.0, 59.0]48426.1%
 
(28.0, 41.0]44023.8%
 
(59.0, 100.0]43523.5%
 
2020-11-30T19:58:31.744184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:31.854327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:32.019890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.50081037
Min length12

TLCar
Categorical

UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
(999.999, 1265.0]
464 
(1522.0, 1765.0]
463 
(1765.0, 1999.0]
462 
(1265.0, 1522.0]
462 
ValueCountFrequency (%) 
(999.999, 1265.0]46425.1%
 
(1522.0, 1765.0]46325.0%
 
(1765.0, 1999.0]46225.0%
 
(1265.0, 1522.0]46225.0%
 
2020-11-30T19:58:32.177083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:32.283362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:32.428028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length16
Mean length16.25067531
Min length16

TLHGV
Categorical

UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
(499.999, 621.0]
464 
(745.0, 871.0]
463 
(621.0, 745.0]
462 
(871.0, 999.0]
462 
ValueCountFrequency (%) 
(499.999, 621.0]46425.1%
 
(745.0, 871.0]46325.0%
 
(621.0, 745.0]46225.0%
 
(871.0, 999.0]46225.0%
 
2020-11-30T19:58:32.599193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:32.715764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:32.838249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length14
Mean length14.50135062
Min length14

Strasse
Categorical

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
A3
559 
A9
466 
A96
155 
A7
130 
A73
129 
Other values (12)
412 
ValueCountFrequency (%) 
A355930.2%
 
A946625.2%
 
A961558.4%
 
A71307.0%
 
A731297.0%
 
A61276.9%
 
A991166.3%
 
A92663.6%
 
A94372.0%
 
A70311.7%
 
Other values (7)351.9%
 
2020-11-30T19:58:32.986091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-11-30T19:58:33.161143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.30902215
Min length2

Kat
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
3
881 
7
718 
2
216 
1
 
36
ValueCountFrequency (%) 
388147.6%
 
771838.8%
 
221611.7%
 
1361.9%
 
2020-11-30T19:58:33.352257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:33.463583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:33.589154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.048082118
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-11-30T19:58:33.769775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.951675744
Coefficient of variation (CV)0.3866172734
Kurtosis0.1886709966
Mean5.048082118
Median Absolute Deviation (MAD)0
Skewness-1.379065019
Sum9344
Variance3.809038212
MonotocityNot monotonic
2020-11-30T19:58:33.910763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
6129970.2%
 
130016.2%
 
31206.5%
 
71176.3%
 
5110.6%
 
440.2%
 
ValueCountFrequency (%) 
130016.2%
 
31206.5%
 
440.2%
 
5110.6%
 
6129970.2%
 
ValueCountFrequency (%) 
71176.3%
 
6129970.2%
 
5110.6%
 
440.2%
 
31206.5%
 

Betei
Real number (ℝ≥0)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.276607239
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-11-30T19:58:34.037728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9626475579
Coefficient of variation (CV)0.422843054
Kurtosis42.7901568
Mean2.276607239
Median Absolute Deviation (MAD)0
Skewness3.802231857
Sum4214
Variance0.9266903208
MonotocityNot monotonic
2020-11-30T19:58:34.324331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2113361.2%
 
335519.2%
 
121811.8%
 
41035.6%
 
5251.4%
 
760.3%
 
660.3%
 
840.2%
 
1810.1%
 
ValueCountFrequency (%) 
121811.8%
 
2113361.2%
 
335519.2%
 
41035.6%
 
5251.4%
 
ValueCountFrequency (%) 
1810.1%
 
840.2%
 
760.3%
 
660.3%
 
5251.4%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.388438682
Minimum0
Maximum9
Zeros63
Zeros (%)3.4%
Memory size14.5 KiB
2020-11-30T19:58:34.430774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.441722098
Coefficient of variation (CV)0.7206038909
Kurtosis0.3305547855
Mean3.388438682
Median Absolute Deviation (MAD)1
Skewness1.276964923
Sum6272
Variance5.962006804
MonotocityNot monotonic
2020-11-30T19:58:34.521327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
283144.9%
 
344724.1%
 
81658.9%
 
91246.7%
 
5904.9%
 
1864.6%
 
0633.4%
 
7351.9%
 
660.3%
 
440.2%
 
ValueCountFrequency (%) 
0633.4%
 
1864.6%
 
283144.9%
 
344724.1%
 
440.2%
 
ValueCountFrequency (%) 
91246.7%
 
81658.9%
 
7351.9%
 
660.3%
 
5904.9%
 

UArt2
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.188006483
Minimum-1
Maximum9
Zeros4
Zeros (%)0.2%
Memory size14.5 KiB
2020-11-30T19:58:34.622063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.063346849
Coefficient of variation (CV)16.29383626
Kurtosis3.611958471
Mean0.188006483
Median Absolute Deviation (MAD)0
Skewness2.328552188
Sum348
Variance9.384093916
MonotocityNot monotonic
2020-11-30T19:58:34.720250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1158485.6%
 
91337.2%
 
8693.7%
 
3382.1%
 
2110.6%
 
150.3%
 
740.2%
 
040.2%
 
520.1%
 
410.1%
 
ValueCountFrequency (%) 
-1158485.6%
 
040.2%
 
150.3%
 
2110.6%
 
3382.1%
 
ValueCountFrequency (%) 
91337.2%
 
8693.7%
 
740.2%
 
520.1%
 
410.1%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.866558617
Minimum0
Maximum89
Zeros1660
Zeros (%)89.7%
Memory size14.5 KiB
2020-11-30T19:58:34.824620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.27724837
Coefficient of variation (CV)2.959012893
Kurtosis5.09890963
Mean7.866558617
Median Absolute Deviation (MAD)0
Skewness2.646851476
Sum14561
Variance541.8302919
MonotocityNot monotonic
2020-11-30T19:58:34.923389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0166089.7%
 
73955.1%
 
72412.2%
 
89181.0%
 
82130.7%
 
8880.4%
 
8140.2%
 
8630.2%
 
8320.1%
 
7520.1%
 
Other values (5)50.3%
 
ValueCountFrequency (%) 
0166089.7%
 
72412.2%
 
73955.1%
 
7520.1%
 
7610.1%
 
ValueCountFrequency (%) 
89181.0%
 
8880.4%
 
8710.1%
 
8630.2%
 
8410.1%
 

AUrs2
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4667747164
Minimum0
Maximum89
Zeros1840
Zeros (%)99.4%
Memory size14.5 KiB
2020-11-30T19:58:35.019484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.050807703
Coefficient of variation (CV)12.96301511
Kurtosis166.5196743
Mean0.4667747164
Median Absolute Deviation (MAD)0
Skewness12.94672183
Sum864
Variance36.61227386
MonotocityNot monotonic
2020-11-30T19:58:35.105994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
0184099.4%
 
7330.2%
 
8120.1%
 
8020.1%
 
7520.1%
 
8910.1%
 
8410.1%
 
ValueCountFrequency (%) 
0184099.4%
 
7330.2%
 
7520.1%
 
8020.1%
 
8120.1%
 
ValueCountFrequency (%) 
8910.1%
 
8410.1%
 
8120.1%
 
8020.1%
 
7520.1%
 

AufHi
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02106969206
Minimum-1
Maximum9
Zeros2
Zeros (%)0.1%
Memory size14.5 KiB
2020-11-30T19:58:35.197709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile3
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.843055378
Coefficient of variation (CV)87.4742437
Kurtosis0.636765015
Mean0.02106969206
Median Absolute Deviation (MAD)0
Skewness1.407118823
Sum39
Variance3.396853125
MonotocityNot monotonic
2020-11-30T19:58:35.288390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-1140275.7%
 
338020.5%
 
4442.4%
 
5160.9%
 
830.2%
 
920.1%
 
020.1%
 
210.1%
 
110.1%
 
ValueCountFrequency (%) 
-1140275.7%
 
020.1%
 
110.1%
 
210.1%
 
338020.5%
 
ValueCountFrequency (%) 
920.1%
 
830.2%
 
5160.9%
 
4442.4%
 
338020.5%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1826 
1
 
25
ValueCountFrequency (%) 
-1182698.6%
 
1251.4%
 
2020-11-30T19:58:35.397175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:35.474321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:35.551992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.986493787
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5164775797
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-11-30T19:58:35.651736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile4.5
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.61349837
Coefficient of variation (CV)-3.124043393
Kurtosis8.099901466
Mean-0.5164775797
Median Absolute Deviation (MAD)0
Skewness3.130320983
Sum-956
Variance2.603376991
MonotocityNot monotonic
2020-11-30T19:58:35.745230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-1169491.5%
 
5603.2%
 
4563.0%
 
6331.8%
 
280.4%
 
ValueCountFrequency (%) 
-1169491.5%
 
280.4%
 
4563.0%
 
5603.2%
 
6331.8%
 
ValueCountFrequency (%) 
6331.8%
 
5603.2%
 
4563.0%
 
280.4%
 
-1169491.5%
 

Char2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1809 
6
 
42
ValueCountFrequency (%) 
-1180997.7%
 
6422.3%
 
2020-11-30T19:58:35.872635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:35.953151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:36.035302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.977309562
Min length1

Bes1
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1488 
6
357 
1
 
6
ValueCountFrequency (%) 
-1148880.4%
 
635719.3%
 
160.3%
 
2020-11-30T19:58:36.158505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:36.255215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:36.413988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.803889789
Min length1

Bes2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1850 
6
 
1
ValueCountFrequency (%) 
-1185099.9%
 
610.1%
 
2020-11-30T19:58:36.548348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-11-30T19:58:36.636107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:36.714822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.999459751
Min length1

Lich1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
0
1489 
2
262 
1
 
97
-1
 
3
ValueCountFrequency (%) 
0148980.4%
 
226214.2%
 
1975.2%
 
-130.2%
 
2020-11-30T19:58:36.835937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:37.046663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:37.163230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.001620746
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1492 
4
343 
3
 
16
ValueCountFrequency (%) 
-1149280.6%
 
434318.5%
 
3160.9%
 
2020-11-30T19:58:37.316406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:37.410897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:37.524085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.806050783
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
0
1393 
1
413 
2
 
40
-1
 
5
ValueCountFrequency (%) 
0139375.3%
 
141322.3%
 
2402.2%
 
-150.3%
 
2020-11-30T19:58:37.660823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:37.749743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:37.856796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002701243
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1834 
2
 
17
ValueCountFrequency (%) 
-1183499.1%
 
2170.9%
 
2020-11-30T19:58:37.983838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:38.065507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:38.151324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990815775
Min length1

Fstf
Categorical

MISSING

Distinct7
Distinct (%)0.4%
Missing109
Missing (%)5.9%
Memory size14.5 KiB
2
804 
1
580 
3
290 
4
 
38
S
 
22
Other values (2)
 
8
ValueCountFrequency (%) 
280443.4%
 
158031.3%
 
329015.7%
 
4382.1%
 
S221.2%
 
550.3%
 
F30.2%
 
(Missing)1095.9%
 
2020-11-30T19:58:38.286057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:38.380842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:38.503722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.117774176
Min length1

WoTag
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
Fr
330 
Do
293 
Mi
287 
Di
280 
Mo
255 
Other values (3)
406 
ValueCountFrequency (%) 
Fr33017.8%
 
Do29315.8%
 
Mi28715.5%
 
Di28015.1%
 
Mo25513.8%
 
So20411.0%
 
Sa19010.3%
 
120.6%
 
2020-11-30T19:58:38.636914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:38.725008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:38.855115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.987034036
Min length0

FeiTag
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1804 
1
 
47
ValueCountFrequency (%) 
-1180497.5%
 
1472.5%
 
2020-11-30T19:58:38.975341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:39.047483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:39.123948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.97460832
Min length1

Month
Categorical

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
Jul
238 
Aug
220 
Oct
166 
Sep
162 
Jun
158 
Other values (7)
907 
ValueCountFrequency (%) 
Jul23812.9%
 
Aug22011.9%
 
Oct1669.0%
 
Sep1628.8%
 
Jun1588.5%
 
Apr1508.1%
 
Mar1427.7%
 
Nov1417.6%
 
May1377.4%
 
Dec1377.4%
 
Other values (2)20010.8%
 
2020-11-30T19:58:39.265860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T19:58:39.424917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-11-30T19:58:12.694311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:12.824634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.023923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.160129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.273843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.395355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.512065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.633020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.742806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.867656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:13.995451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.118681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.228848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.354852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.487003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.602034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.712496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.855588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:14.983771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.087955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.196219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.299157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.413457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.535506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.678381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.848951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:15.998276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:16.176166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:16.329983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:16.479119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:16.729122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:16.850199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:16.963188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.082845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.195978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.302309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.419463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.526727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.647336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.753935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.874941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:17.978826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.082073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.187186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.297862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.413871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.523471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.651065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.761026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.887351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:18.994940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.102755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.210083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.316686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.423739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.537803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.649048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.746736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:19.962845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.072287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.174871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.270633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.367284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.461792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.556576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.660385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.766696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.872008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:20.970804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.085115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.220210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.340971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.441514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.540434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.645339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.743582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.843369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:21.943473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.047310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.147408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.253944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.355290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.459033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.550940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.672319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.783166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.880776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:22.973643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.181565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.306499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.404913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.512889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.644800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.761370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.872086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:23.980471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.101348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.207578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.309369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.425584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.529784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.636592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.746325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:24.886948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.007087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.107220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.201519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.300411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.402316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.514590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.662788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.821158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:25.959294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:26.089412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:26.218854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:26.337090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:26.452847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:26.668801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:26.782981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:26.895150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:27.003482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-30T19:58:39.619841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-30T19:58:39.912808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-30T19:58:40.319246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-30T19:58:40.605486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-30T19:58:40.947053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-30T19:58:27.344723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:28.538161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T19:58:28.783092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
00(211.5, 1341.0](90.0, 1326.0](4506.0, 8335.0](3387.0, 5264.0]00(59.0, 100.0](1522.0, 1765.0](621.0, 745.0]A32132-100-1-1-1-1-1-10-11-12Di1Jan
11(8.999, 69.0](2.999, 32.0](4506.0, 8335.0](3387.0, 5264.0]100(28.0, 41.0](1522.0, 1765.0](621.0, 745.0]A63632-1890-1-1-1-1-1-10-10-12Di1Jan
22(117.0, 211.5](55.0, 90.0](8335.0, 14367.0](5264.0, 17805.0]00(41.0, 59.0](1265.0, 1522.0](745.0, 871.0]A336529003-1-1-1-1-10-11-12Mi-1Jan
33(117.0, 211.5](55.0, 90.0](8335.0, 14367.0](5264.0, 17805.0]1996(41.0, 59.0](1265.0, 1522.0](745.0, 871.0]A33672-1820-1-1-1-1-1-10-11-12Mi-1Jan
44(117.0, 211.5](32.0, 55.0](14367.0, 49765.0](5264.0, 17805.0]00(4.999, 28.0](1265.0, 1522.0](499.999, 621.0]A33622-100-1-1-1-1-1-10-10-1NaNMi-1Jan
55(8.999, 69.0](2.999, 32.0](4506.0, 8335.0](2002.5, 3387.0]00(41.0, 59.0](999.999, 1265.0](745.0, 871.0]A63632-100-11-1-1-1-10-10-11Mi-1Jan
66(211.5, 1341.0](90.0, 1326.0](14367.0, 49765.0](3387.0, 5264.0]00(4.999, 28.0](999.999, 1265.0](621.0, 745.0]A97133-1720-1-1-1-1-1-10-1121Mi-1Jan
77(211.5, 1341.0](90.0, 1326.0](8335.0, 14367.0](5264.0, 17805.0]110(41.0, 59.0](1522.0, 1765.0](621.0, 745.0]A33733-100-1-1-1-1-1-1241-12Do-1Jan
88(117.0, 211.5](32.0, 55.0](4506.0, 8335.0](2002.5, 3387.0]00(41.0, 59.0](1765.0, 1999.0](871.0, 999.0]A97123-100-1-1-1-1-1-1240-11Fr-1Jan
99(69.0, 117.0](55.0, 90.0](8335.0, 14367.0](2002.5, 3387.0]0112(4.999, 28.0](1522.0, 1765.0](871.0, 999.0]A93632-100-1-1-1-1-1-1241-14Fr-1Jan

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
18411857(69.0, 117.0](55.0, 90.0](831.999, 4506.0](2002.5, 3387.0]80(59.0, 100.0](1265.0, 1522.0](499.999, 621.0]A73119-1003-16-1-1-1240-12Fr-1Dec
18421858(211.5, 1341.0](32.0, 55.0](14367.0, 49765.0](5264.0, 17805.0]00(4.999, 28.0](1265.0, 1522.0](745.0, 871.0]A93632-100-1-1-1-1-1-10-10-1FFr-1Dec
18431859(211.5, 1341.0](90.0, 1326.0](14367.0, 49765.0](5264.0, 17805.0]00(4.999, 28.0](1765.0, 1999.0](621.0, 745.0]A731429003-1-1-1-1-10-10-12-1Dec
18441860(117.0, 211.5](90.0, 1326.0](14367.0, 49765.0](3387.0, 5264.0]160(28.0, 41.0](1765.0, 1999.0](499.999, 621.0]A73622-100-1-1-1-1-1-10-10-11Sa-1Dec
18451861(8.999, 69.0](2.999, 32.0](831.999, 4506.0](2002.5, 3387.0]220(59.0, 100.0](999.999, 1265.0](871.0, 999.0]A93622-100-1-1-1-1-1-10-10-11So-1Dec
18461862(8.999, 69.0](2.999, 32.0](8335.0, 14367.0](5264.0, 17805.0]110(59.0, 100.0](1765.0, 1999.0](499.999, 621.0]A93734-100-1-1-1-1-1-1240-13So-1Dec
18471863(117.0, 211.5](32.0, 55.0](4506.0, 8335.0](2002.5, 3387.0]12750(41.0, 59.0](1765.0, 1999.0](499.999, 621.0]A97622-100-1-1-1-16-10-10-11So-1Dec
18481864(69.0, 117.0](55.0, 90.0](831.999, 4506.0](3387.0, 5264.0]00(59.0, 100.0](1265.0, 1522.0](621.0, 745.0]A923622-100-11-1-1-1-1240-12So-1Dec
18491865(69.0, 117.0](55.0, 90.0](831.999, 4506.0](2002.5, 3387.0]40(59.0, 100.0](1765.0, 1999.0](871.0, 999.0]A33632-100-1-1-1-1-1-1230-12-1Dec
18501866(8.999, 69.0](55.0, 90.0](831.999, 4506.0](134.999, 2002.5]60(59.0, 100.0](999.999, 1265.0](499.999, 621.0]A712622-100-1-1-1-1-1-10-10-11Di-1Dec